


"""simple docstring"""



import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase=13      ,	UpperCAmelCase=7      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True      ,	UpperCAmelCase=99      ,	UpperCAmelCase=32      ,	UpperCAmelCase=5      ,	UpperCAmelCase=4      ,	UpperCAmelCase=37      ,	UpperCAmelCase="gelu"      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=512      ,	UpperCAmelCase=16      ,	UpperCAmelCase=2      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=3      ,	UpperCAmelCase=4      ,	UpperCAmelCase=None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			parent
					lowerCAmelCase_      							=			batch_size
					lowerCAmelCase_      							=			seq_length
					lowerCAmelCase_      							=			is_training
					lowerCAmelCase_      							=			use_input_mask
					lowerCAmelCase_      							=			use_token_type_ids
					lowerCAmelCase_      							=			use_labels
					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_probs_dropout_prob
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			type_vocab_size
					lowerCAmelCase_      							=			type_sequence_label_size
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			num_labels
					lowerCAmelCase_      							=			num_choices
					lowerCAmelCase_      							=			scope







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size   )

					lowerCAmelCase_      							=			None
					if self.use_input_mask:
						lowerCAmelCase_      							=			random_attention_mask([self.batch_size, self.seq_length]   )

					lowerCAmelCase_      							=			None
					if self.use_token_type_ids:
						lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size   )

					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			None
					if self.use_labels:
						lowerCAmelCase_      							=			ids_tensor([self.batch_size]      ,	self.type_sequence_label_size   )
						lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.seq_length]      ,	self.num_labels   )
						lowerCAmelCase_      							=			ids_tensor([self.batch_size]      ,	self.num_choices   )

					lowerCAmelCase_      							=			self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return LlamaConfig(
					    vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	is_decoder=UpperCAmelCase      ,	initializer_range=self.initializer_range      ,	)







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			LlamaModel(config=UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	attention_mask=UpperCAmelCase   )
					lowerCAmelCase_      							=			model(UpperCAmelCase   )
					self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			True
					lowerCAmelCase_      							=			LlamaModel(UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(
					    UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	encoder_hidden_states=UpperCAmelCase      ,	encoder_attention_mask=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			model(
					    UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	encoder_hidden_states=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	attention_mask=UpperCAmelCase   )
					self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			LlamaForCausalLM(config=UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	labels=UpperCAmelCase   )
					self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.vocab_size)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			True
					lowerCAmelCase_      							=			True
					lowerCAmelCase_      							=			LlamaForCausalLM(config=UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()

					# first forward pass
					lowerCAmelCase_      							=			model(
					    UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	encoder_hidden_states=UpperCAmelCase      ,	encoder_attention_mask=UpperCAmelCase      ,	use_cache=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					lowerCAmelCase_      							=			ids_tensor((self.batch_size, 3)      ,	config.vocab_size   )
					lowerCAmelCase_      							=			ids_tensor((self.batch_size, 3)      ,	vocab_size=2   )

					# append to next input_ids and
					lowerCAmelCase_      							=			torch.cat([input_ids, next_tokens]      ,	dim=-1   )
					lowerCAmelCase_      							=			torch.cat([input_mask, next_mask]      ,	dim=-1   )

					lowerCAmelCase_      							=			model(
					    UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	encoder_hidden_states=UpperCAmelCase      ,	encoder_attention_mask=UpperCAmelCase      ,	output_hidden_states=UpperCAmelCase      ,	)['''hidden_states'''][0]
					lowerCAmelCase_      							=			model(
					    UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	encoder_hidden_states=UpperCAmelCase      ,	encoder_attention_mask=UpperCAmelCase      ,	past_key_values=UpperCAmelCase      ,	output_hidden_states=UpperCAmelCase      ,	)['''hidden_states'''][0]

					# select random slice
					lowerCAmelCase_      							=			ids_tensor((1,)      ,	output_from_past.shape[-1]   ).item()
					lowerCAmelCase_      							=			output_from_no_past[:, -3:, random_slice_idx].detach()
					lowerCAmelCase_      							=			output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-3   )   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	(
					    lowerCAmelCase_
					)							,	
					)      							=			config_and_inputs
					lowerCAmelCase_      							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict







@require_torch
class    __a						( __snake_case					,			__snake_case					,			__snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       int			       =(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				lowerCamelCase     :       int			       =(LlamaForCausalLM,) if is_torch_available() else ()
				lowerCamelCase     :       Tuple			       =(
				    {
				        'feature-extraction': LlamaModel,
				        'text-classification': LlamaForSequenceClassification,
				        'text-generation': LlamaForCausalLM,
				        'zero-shot': LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCamelCase     :       Optional[int]			       =False
				lowerCamelCase     :       Optional[int]			       =False







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			LlamaModelTester(self   )
					lowerCAmelCase_      							=			ConfigTester(self      ,	config_class=UpperCAmelCase      ,	hidden_size=37   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					self.config_tester.run_common_tests()







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						lowerCAmelCase_      							=			type
						self.model_tester.create_and_check_model(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase_      							=			3
					lowerCAmelCase_      							=			input_dict['''input_ids''']
					lowerCAmelCase_      							=			input_ids.ne(1   ).to(UpperCAmelCase   )
					lowerCAmelCase_      							=			ids_tensor([self.model_tester.batch_size]      ,	self.model_tester.type_sequence_label_size   )
					lowerCAmelCase_      							=			LlamaForSequenceClassification(UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	labels=UpperCAmelCase   )
					self.assertEqual(result.logits.shape      ,	(self.model_tester.batch_size, self.model_tester.num_labels)   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase_      							=			3
					lowerCAmelCase_      							=			'''single_label_classification'''
					lowerCAmelCase_      							=			input_dict['''input_ids''']
					lowerCAmelCase_      							=			input_ids.ne(1   ).to(UpperCAmelCase   )
					lowerCAmelCase_      							=			ids_tensor([self.model_tester.batch_size]      ,	self.model_tester.type_sequence_label_size   )
					lowerCAmelCase_      							=			LlamaForSequenceClassification(UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	labels=UpperCAmelCase   )
					self.assertEqual(result.logits.shape      ,	(self.model_tester.batch_size, self.model_tester.num_labels)   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase_      							=			3
					lowerCAmelCase_      							=			'''multi_label_classification'''
					lowerCAmelCase_      							=			input_dict['''input_ids''']
					lowerCAmelCase_      							=			input_ids.ne(1   ).to(UpperCAmelCase   )
					lowerCAmelCase_      							=			ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]      ,	self.model_tester.type_sequence_label_size   ).to(torch.float   )
					lowerCAmelCase_      							=			LlamaForSequenceClassification(UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	labels=UpperCAmelCase   )
					self.assertEqual(result.logits.shape      ,	(self.model_tester.batch_size, self.model_tester.num_labels)   )







				@unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







				@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase_      							=			ids_tensor([1, 10]      ,	config.vocab_size   )
					lowerCAmelCase_      							=			ids_tensor([1, int(config.max_position_embeddings * 1.5   )]      ,	config.vocab_size   )

					set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
					lowerCAmelCase_      							=			LlamaModel(UpperCAmelCase   )
					original_model.to(UpperCAmelCase   )
					original_model.eval()
					lowerCAmelCase_      							=			original_model(UpperCAmelCase   ).last_hidden_state
					lowerCAmelCase_      							=			original_model(UpperCAmelCase   ).last_hidden_state

					set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
					lowerCAmelCase_      							=			{'''type''': scaling_type, '''factor''': 1_0.0}
					lowerCAmelCase_      							=			LlamaModel(UpperCAmelCase   )
					scaled_model.to(UpperCAmelCase   )
					scaled_model.eval()
					lowerCAmelCase_      							=			scaled_model(UpperCAmelCase   ).last_hidden_state
					lowerCAmelCase_      							=			scaled_model(UpperCAmelCase   ).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
						self.assertTrue(torch.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-5   )   )
					else:
						self.assertFalse(torch.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-5   )   )

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-5   )   )







@require_torch
class    __a						( unittest.TestCase							):







				@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''   )
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
					lowerCAmelCase_      							=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''      ,	device_map='''auto'''   )
					lowerCAmelCase_      							=			model(torch.tensor([input_ids]   )   )
					# Expected mean on dim = -1
					lowerCAmelCase_      							=			torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]   )
					torch.testing.assert_close(out.mean(-1   )      ,	UpperCAmelCase      ,	atol=1E-2      ,	rtol=1E-2   )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					lowerCAmelCase_      							=			torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]   )
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30]      ,	UpperCAmelCase      ,	atol=1E-5      ,	rtol=1E-5   )







				@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''   )
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
					lowerCAmelCase_      							=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''      ,	device_map='''auto'''   )
					lowerCAmelCase_      							=			model(torch.tensor(UpperCAmelCase   )   )
					# Expected mean on dim = -1
					lowerCAmelCase_      							=			torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]   )
					torch.testing.assert_close(out.mean(-1   )      ,	UpperCAmelCase      ,	atol=1E-2      ,	rtol=1E-2   )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					lowerCAmelCase_      							=			torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]   )
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30]      ,	UpperCAmelCase      ,	atol=1E-5      ,	rtol=1E-5   )







				@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''   )
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
					lowerCAmelCase_      							=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''      ,	device_map='''auto'''   )
					lowerCAmelCase_      							=			model(torch.tensor(UpperCAmelCase   )   )
					# Expected mean on dim = -1
					lowerCAmelCase_      							=			torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]   )
					torch.testing.assert_close(out.mean(-1   )      ,	UpperCAmelCase      ,	atol=1E-2      ,	rtol=1E-2   )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					lowerCAmelCase_      							=			torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]   )
					# fmt: on
					torch.testing.assert_close(out.mean(-1   )      ,	UpperCAmelCase      ,	atol=1E-2      ,	rtol=1E-2   )







				@unittest.skip(
				    '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'''   )
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
					lowerCAmelCase_      							=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''      ,	device_map='''auto'''   )
					lowerCAmelCase_      							=			model(torch.tensor(UpperCAmelCase   )   )

					lowerCAmelCase_      							=			torch.tensor(
					    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]      ,	dtype=torch.floataa   )
					torch.testing.assert_close(out.mean(-1   )      ,	UpperCAmelCase      ,	atol=1E-2      ,	rtol=1E-2   )
					# fmt: off
					lowerCAmelCase_      							=			torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]   )
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30]      ,	UpperCAmelCase      ,	atol=1E-5      ,	rtol=1E-5   )







				@unittest.skip('''Model is curently gated'''   )
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
					lowerCAmelCase_      							=			'''Simply put, the theory of relativity states that '''
					lowerCAmelCase_      							=			LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''   )
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase      ,	return_tensors='''pt'''   )
					lowerCAmelCase_      							=			LlamaForCausalLM.from_pretrained(
					    '''meta-llama/Llama-2-13b-chat-hf'''      ,	device_map='''sequential'''      ,	use_safetensors=UpperCAmelCase   )

					# greedy generation outputs
					lowerCAmelCase_      							=			model.generate(UpperCAmelCase      ,	max_new_tokens=64      ,	top_p=UpperCAmelCase      ,	temperature=1      ,	do_sample=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(generated_ids[0]      ,	skip_special_tokens=UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )



"""simple docstring"""



from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class    __a						( __snake_case							):







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = None      ,	UpperCAmelCase = True      ,	UpperCAmelCase = None      ,	UpperCAmelCase = False      ,	UpperCAmelCase = None      ,	UpperCAmelCase = True      ,	UpperCAmelCase = "arrow"      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(
					    split=UpperCAmelCase      ,	features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	keep_in_memory=UpperCAmelCase      ,	streaming=UpperCAmelCase      ,	**UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			load_from_cache_file
					lowerCAmelCase_      							=			file_format
					lowerCAmelCase_      							=			Spark(
					    df=UpperCAmelCase      ,	features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	working_dir=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.streaming:
						return self.builder.as_streaming_dataset(split=self.split   )
					lowerCAmelCase_      							=			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
					self.builder.download_and_prepare(
					    download_mode=UpperCAmelCase      ,	file_format=self._file_format      ,	)
					return self.builder.as_dataset(split=self.split   )